With the continuous development of computer technology and communications technology, more and more users are searching, looking up, and buying products on online shopping websites. Before searching, looking up and buying products, buyer-users are likely to browse the product information posted on websites by seller-users. The seller-users can be corporate entities, manufacturers or individual operators.
The volume of product information submitted by seller-users to the online marketplace website servers can be massive. In order to classify the products represented by the product information received and effectively guide buyer-users to obtain the desired information, website servers usually partition product information using multilevel categories. Multilevel category systems generally have the following characteristics listed below.
First, multilevel category system architecture is relatively stable. Multilevel category systems with relatively stable architecture help seller-users to get accustomed to the system. In such systems, when product information is submitted to website servers, product information is submitted in a standard format and content in accordance with the requirements of the multilevel category system. Such systems also help buyer-users to get accustomed to the system so that the buyer-user can accumulate experience in rapidly searching for desired product information.
Second, multilevel category systems are generally operated and maintained manually by website server operations personnel. With the manual operation method, common knowledge in the field can be regularized to form standards, aiding the extension of the use of the multilevel category system to a variety of websites.
In order to preserve the two characteristics of multilevel category systems described above, when partitioning massive volumes of product information using multilevel categories, product information typically can only be partitioned with a relatively coarse degree of granularity. This is because, due to the wide variety of product information, if product information is partitioned to a finer degree of granularity, then the bottom layer (also referred to as leaf categories) of the multilevel categories must change as product information changes, which is detrimental to the stability of the multilevel category system; moreover, if product information is partitioned to a fine degree of granularity, the resulting multilevel category architecture is bound to be enormous, increasing the difficulty of manual operation of the website servers.
For example, assume the product information under a particular leaf category is “dresses,” and the information for a particular dress is of interest. When the product material associated with the product information changes from silk to cotton, the information for the dress will remain under the leaf category for dresses; no change needs to be made to the leaf category. If, however, the granularity of the partitioning of the multilevel category system is finer and the product information under a particular leaf category is “silk dresses,” then when the product material of the information for a particular dress changes to cotton, it is necessary to switch the product information from the leaf category for silk dresses to the leaf category for cotton dresses. In other words, the leaf categories change as product information changes. At the same time, because multilevel category systems typically use tree node architecture, each time a subcategory is added, a large volume of categories is added to the multilevel category system, making the architecture of multilevel category systems very large.
Because the granularity of product information partitioning in multilevel category systems is relatively fine, the volume of product information encompassed at even the lowest layer in the multilevel category system can be massive. Under these conditions, when buyer-users search or query product information using multilevel category systems, query time can be long, and query accuracy is lower; moreover, when website servers recommend product information to buyer-users, they are often only able to do so at the leaf category level, resulting in substantial differences in the recommended product information, such that the accuracy of the recommendation does not meet the actual needs of buyer-users.
Additionally, due to the large volume of product information encompassed in leaf categories, the differences in product information included in the same leaf category are also great; therefore, the level of difficulty in realizing operations regarding product information under leaf categories is also higher. For example, website operators may want to identify information for unsafe/counterfeit products using the price parameter. It is generally acknowledged that extremely low prices are very likely to be suspect as counterfeit products. An operator may devise a rule that if the price for a Brand A product is below $100, then the product is likely a counterfeit product, and that if the price is below $20 for another product of the same type but is a Brand B product, the product is likely to be a counterfeit product. If the price of a particular product is $50, it becomes more difficult to directly appraise whether the product is a counterfeit product using the price parameter rule. Additional information for the product would be required to determine its authenticity status. In practice, the large volume of product information under the leaf categories results in an extremely large amount of operations.